Lifeboat Foundation

To set some benchmarks for their simulator, the researchers tried out three different design algorithms working in conjunction with a deep reinforcement learning algorithm that learned to control the robots through many rounds of trial and error.

The co-designed bots performed well on the simpler tasks, like walking or carrying things, but struggled with tougher challenges, like catching and lifting, suggesting there’s plenty of scope for advances in co-design algorithms. Nonetheless, the AI-designed bots outperformed ones design by humans on almost every task.

Continue reading “Robots Evolve Bodies and Brains Like Animals in MIT’s New AI Training Simulator” »

In recent years, computational tools based on reinforcement learning have achieved remarkable results in numerous tasks, including image classification and robotic object manipulation. Meanwhile, computer scientists have also been training reinforcement learning models to play specific human games and videogames.

To challenge research teams working on reinforcement learning techniques, the Neural Information Processing Systems (NeurIPS) annual conference introduced the MineRL competition, a contest in which different algorithms are tested on the same task in Minecraft, the renowned computer game developed by Mojang Studios. More specifically, contestants are asked to create algorithms that will need to obtain a diamond from raw pixels in the Minecraft game.

The algorithms can only be trained for four days and on 8,000,000 samples created by the MineRL simulator, using a single GPU machine. In addition to the training dataset, participants are also provided with a large collection of human demonstrations (i.e., video frames in which the task is solved by human players).

Working with two teams of mathematicians, DeepMind engineered an algorithm that can look across different mathematical fields and spot connections that previously escaped the human mind. The AI doesn’t do all the work—when fed sufficient data, it finds patterns. These patterns are then passed on to human mathematicians to guide their intuition and creativity towards new laws of nature.

“I was not expecting to have some of my preconceptions turned on their head,” said Dr. Marc Lackenby at the University of Oxford, one of the scientists collaborating with DeepMind, to Nature, where the study was published.

The AI comes just a few months after DeepMind’s previous triumph in solving a 50-year-old challenge in biology. This is different. For the first time, machine learning is aiming at the core of mathematics—a science for spotting patterns that eventually leads to formally-proven ideas, or theorems, about how our world works. It also emphasized collaboration between machine and man in bridging observations to working theorems.

Machine learning projects are much more complicated and bigger than machine learning model algorithms.

Meta, the company formerly known as Facebook is pulling the plug on its facial recognition program. The company is planning to delete more than one billion people’s individual facial recognition templates, and will no longer automatically recognize people’s faces in photos or videos as a result of this change, according to its own post. The use of facial recognition technology has a disparate impact on people of color, disenfranchising a group who already face inequality, and Facebook seems to be acknowledging this inherent harm. The Breakdown You Need to Know.

CultureBanx reported that Meta seems to always be embroiled in corporate drama and with intense scrutiny. When you add that to the growing concern from users and regulators that facial recognition space remains complicated, an exit makes sense. More than 600 million daily active users on Facebook had opted into the use of the face recognition technology.

Research shows commercial artificial intelligence systems tend to have higher error rates for women and black people. Some facial recognition systems would only confuse light-skin men 0.8% of the time and would have an error rate of 34.7% for dark-skin women. Just imagine surveillance being used with these flawed algorithms. A 2018 IDC report noted it expects worldwide spending on cognitive and AI systems to reach $77.6 billion in 2022.

Blackholes are a breakdown in the equations of spacetime. This means both space and time no longer behave the way we would expect of them.
Today we explore the breakdown in time around blackholes and what it means to interact with the event horizon, or the place where time appears to stand still.

Further Reading/Consumption:

Continue reading “Why Time ‘Stops’ in a Black Hole” »

Do you know what the Earth’s atmosphere is made of? You’d probably remember it’s oxygen, and maybe nitrogen. And with a little help from Google you can easily reach a more precise answer: 78% nitrogen, 21% oxygen and 1% Argon gas. However, when it comes to the composition of exo-atmospheres—the atmospheres of planets outside our solar system—the answer is not known. This is a shame, as atmospheres can indicate the nature of planets, and whether they can host life.

As exoplanets are so far away, it has proven extremely difficult to probe their atmospheres. Research suggests that artificial intelligence (AI) may be our best bet to explore them—but only if we can show that these algorithms think in reliable, scientific ways, rather than cheating the system. Now our new paper, published in The Astrophysical Journal, has provided reassuring insight into their mysterious logic.

Astronomers typically exploit the transit method to investigate exoplanets, which involves measuring dips in light from a star as a planet passes in front of it. If an atmosphere is present on the planet, it can absorb a very tiny bit of light, too. By observing this event at different wavelengths—colors of light—the fingerprints of molecules can be seen in the absorbed starlight, forming recognizable patterns in what we call a spectrum. A typical signal produced by the atmosphere of a Jupiter-sized planet only reduces the stellar light by ~0.01% if the star is Sun-like. Earth-sized planets produce 10–100 times lower signals. It’s a bit like spotting the eye color of a cat from an aircraft.

AI doesn’t actually multitask very well because typical algorithms aren’t very versatile. But a new project from Google could change that.

A New York City law requires algorithms used in hiring to be “audited” for bias. It’s the first in the US—and part of a larger push toward regulation.